Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go

Evaluation of Patients' Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital

This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient's level of walking independence.

A multimodal dataset of real world mobility activities in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson's disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being "on" or "off" medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.

Evaluating the immediate effect of the speed alteration task on walking stability using the Timed Up and Go test

[Purpose] This study aimed to investigate how the speed alteration task, which gradually increases or conversely decreases walking speed, affected walking stability. [Participants and Methods] Thirteen healthy young adults performed two walking tasks as follows: the speed alteration task, in which the walking speed was gradually increased or decreased, and the speed constant task, in which the walking speed was maintained at a comfortable level. Before and after each task, the Timed Up and Go test was performed to analyze time, walking speed, and trajectory. The overall score of the Timed Up and Go test, as well as the scores of the three major segments (i.e., forward, turning around, and return), and nine subsegments, were calculated and analyzed. [Results] During the speed alteration task, parameters including time and walking speed of the Timed Up and Go test were significantly improved. Also, the same parameters increased significantly in the forward and return segments. These increases were also observed in the first subsegment of the forward segment and the second subsegment of the return segment. [Conclusion] The speed alteration task improved walking stability, so it could be used in gait training to improve walking stability.

Factors Associated with Dual-Fluency Walk Speed Improvement after Rehabilitation in Older Patients

Walk speed measured under dual-task conditions (neurocognitive tasks) could reflect patient performance in real-life. Rehabilitation programs are effective in increasing walk speed, but few studies have evaluated the associations between geriatric factors and rehabilitation efficacy under dual-task conditions. Our objective was to investigate the association between geriatric factors and an increase in dual-task walk speed (threshold of 0.1 m/s), after a multidisciplinary rehabilitation program. We performed a retrospective cohort study that included patients aged 75 years and over, who underwent a complete rehabilitation program and who had a neurocognitive assessment at baseline. The primary outcome was the increase in the dual-task (fluency verbal task) walking speed between pre- and post-rehabilitation assessments. In this study, 145 patients were included, with a mean age of 83.6 years old. After rehabilitation, dual-task walk speed increase in 62 (43%) patients. In multivariate analysis, the following factors were associated with an increase in dual-task walk speed: IADL (OR 2.50, 95% CI [1.26; 4.94], p = 0.009), vitamin D level (OR 0.83, 95% CI [0.72; 0.95], p = 0.008), severe sarcopenia (OR 0.00, 95% CI [0.00; 0.32], p = 0.016), depression (OR 15.85, 95% CI [1.32; 190.40], p = 0.029), number of drugs (OR 1.41, 95% CI [1.04; 1.92], p = 0.027), initial dual-fluency walk speed (OR 0.92, 95% CI [0.86; 0.98], p = 0.014) and time interval between initial and final assessments (OR 0.98, 95% CI [0.96; 1.00], p = 0.06). Identifying patients that are less resilient to rehabilitation may promote a centered-patient approach for an individualized and optimized rehabilitation care.

Recording context matters: Differences in gait parameters collected by the OneStep smartphone application

BACKGROUND

Detailed understanding of impairments that underlie walking dysfunction through objective measures is essential to diagnosis, evaluation and care planning. Despite significant developments in motion tracking technologies, there is a dearth of research about the influence of remote monitoring context on performance. The objective of this study was to determine whether gait parameters collected by the OneStep smartphone application differ based on the recording condition.

METHODS

Retrospective repeated measures univariate analysis was performed on data extracted based on detected activity, either spontaneous (background recording) or consciously initiated (in app) walks, of 25 patients enrolled in a physical therapy program.

FINDINGS

Across 7227 walking bouts, significant differences between the two paradigms in velocity (g = 0.48), double support (g = 0.37), stride length (g = 0.37) and step length of the affected side (g = 0.32) were revealed. Overall, the passively recorded walks presented a less clinically favorable spatiotemporal pattern for each of these variables.

INTERPRETATION

The recording context of walks that were used for analysis appears to significantly affect the biomechanical output of the OneStep application. It is unclear whether the disparity found would impact functional recovery of individuals undergoing rehabilitation due to neurological or musculoskeletal disorder. Clinicians may consider this information when incorporating remotely-acquired quantitative gait analysis and interpreting care outcomes as part of therapeutic practice. Future work can further investigate the behavioral and environmental factors contributing to how movement occurs in specific clinical populations when monitored via mobile health systems.

Risk of falls using the Biodex Balance System in non-faller patients with Parkinson Disease

PURPOSE

Biodex Balance System (BBS) is a low-cost platform used to assess balance in different populations. However, no study has used this tool to evaluate the risk of falls related to balance changes in non-faller individuals with Parkinson Disease (PD).

OBJECTIVE

The aim of this study was to determine the changes in the balance in non-faller individuals with mild to moderate PD compared to healthy elders.

METHODS

Forty-six PD patients at stages 2 and 3 were assessed in the 'on' state (fully medicated) as well as 31 age-matched healthy controls. They were submitted to the fall risk protocol of BBS and performed three 20-s trials and a 60-s rest interval between the trials.

RESULTS

Non-faller PD patients had an increased instability when compared to the healthy controls in the anteroposterior (controls: 1.54 ± 1.00 vs. PD patients: 2.91 ± 0.93) and mediolateral directions (controls: 1.21 ± 0.57 vs. PD patients: 1.42 ± 0.46), resulting in a great overall instability in the PD patients (controls: 1.28 ± 0.61 vs. PD patients: 4.09 ± 1.22). A significant correlation between overall instability and UPDRS-III (motor symptoms) in individuals with PD was observed.

CONCLUSION

BBS was able to identify the risk of falls in non-fallers, showing that PD patients have a greater risk of falls in unstable conditions than age-matched healthy elders, mainly due to the large sway in the anteroposterior direction. Furthermore, the severity of motor symptoms was related to overall instability which can increase the risk of falls in PD patients.

Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time-Frequency Representations of the Timed Up and Go Test

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls. However, continuous fall risk monitoring requires extensive healthcare and clinical resources. Our objective is to develop a method suitable for remote and long-term health monitoring of the elderly for mobility impairment and fall risk without the need for an expert. We employed time–frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according to the criteria of the timed up and go test (TUG). The time series signal of the triaxial accelerometer can be transformed by TFA to obtain richer image information. On the basis of the TUG criteria, the semi-supervised SAE model was able to achieve high predictive accuracies of 89.1, 93.4, and 94.1% for the vertical, mediolateral and anteroposterior axes, respectively. We believe that deep learning can be used to analyze triaxial acceleration data, and our work demonstrates its applicability to assessing the mobility and fall risk of the elderly.

Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review

Background

Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

Methods

We performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

Results

The majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

Conclusion

The use of various wearable technologies has enabled researchers and clinicians to monitor patients’ activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients’ lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00863-x.

The timed up & go test sit-to-stand transition: Which signals measured by inertial sensors are a viable route for continuous analysis?

BACKGROUND

The Timed Up and Go test is a well-known clinical test for assessing of mobility and fall risk. It has been shown that the IMU which use an accelerometer and gyroscope are capable of analysing the quantitative parameters of the sit-to-stand transition.

RESEARCH QUESTION

Which signals obtained by the inertial sensors are suitable for continuous Timed Up & Go test sit-to-stand transition analysis?

METHODS

In the study we included 29 older adult volunteers and 31 de-novo Parkinson disease (PD) patients. All subjects performed an instrumented extended TUG wearing a gyro-accelerometer. The sit-to-stand transition was detected from an angular velocity signal. The sit-to-stand signal pattern within the subject group was analyzed via an intra-class correlation between curves. Inter-subjects' variability was visualized using prediction bands.

RESULTS

The angular velocity about the pitch axis exhibited the best signal match across subjects in both groups (0.50 < ICC < 0.75). When analysing acceleration, the acceleration along the antero-posterior axis showed moderate inter-subjects signal pattern match (0.50 < ICC < 0.75) in the reference group. The analysis of other signals revealed a poor signal pattern in both subject groups.

SIGNIFICANCE

For optimal interpretation of the analysis of continuous curves, the signal pattern must be considered. Also, the inter-subject variability along this pattern can be informative and useful.

Prospective Longitudinal Study of Gait and Balance in a Cohort of Elderly Essential Tremor Patients

Background: Essential tremor (ET) encompasses a variety of features, including tremor, cognitive dysfunction, and gait and balance impairments. Gait and balance impairments in ET are often mild, but they can be severe and are, in some cases, associated with functional sequelae in terms of increased fall risk and reduced balance confidence. Previous research on gait and balance in ET has been limited to cross-sectional comparisons. There have been no longitudinal studies or prospective studies. As such, our understanding of natural history and possible predictors of declines in ET-related gait and balance impairments is incomplete. Objectives: We (1) present natural history data on the change in gait and balance measures over time, (2) provide estimates of annual rate of change in each gait and balance metric, and (3) examine the relationship between baseline clinical predictors and changes in gait and balance over time. Methods: 149 ET participants (mean age 78.7 years), enrolled in a prospective, longitudinal, clinical-pathological study, underwent an extensive evaluation of cognition, tremor, and gait and balance at three distinct intervals performed every 18 months. Gait and balance measures included a combination of performance-based tests (e.g., tandem gait, tandem stance) and self-reported assessments (e.g., number of falls, use of a walking aid). Results: Between the baseline and final assessments, numerous balance and gait measures showed evidence of decline and annual rates of change were quantified for each. We examined the predictive utility of clinical variables at baseline for five gait and balance outcomes, with global cognition and executive function standing out as the most consistent predictors. Conclusions: We present a much-needed look into the course of disease for elderly patients with ET, focusing on changes observed in gait and balance and the predictors of these changes. These results also add another dimension to the relevance of cognitive impairment observed in ET; such impairment can now be viewed as predictive of poorer gait and balance over time in ET. These findings are a useful tool for clinicians, patients, and their families to better understand and plan for changing disease-features over time.

Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations

BACKGROUND

One difficulty in turning algorithm design for inertial sensors is detecting two discrete turns in the same direction, close in time. A second difficulty is under-estimation of turn angle due to short-duration hesitations by people with neurological disorders. We aimed to validate and determine the generalizability of a: I. Discrete Turn Algorithm for variable and sequential turns close in time and II: Merged Turn Algorithm for a single turn angle in the presence of hesitations.

METHODS

We validated the Discrete Turn Algorithm with motion capture in healthy controls (HC, n = 10) performing a spectrum of turn angles. Subsequently, the generalizability of the Discrete Turn Algorithm and associated, Merged Turn Algorithm were tested in people with Parkinson's disease (PD, n = 124), spinocerebellar ataxia (SCA, n = 51), and HC (n = 125).

RESULTS

The Discrete Turn Algorithm shows improved agreement with optical motion capture and with known turn angles, compared to our previous algorithm by El-Gohary et al. The Merged Turn algorithm that merges consecutive turns in the same direction with short hesitations resulted in turn angle estimates closer to a fixed 180-degree turn angle in the PD, SCA, and HC subjects compared to our previous turn algorithm. Additional metrics were proposed to capture turn hesitations in PD and SCA.

CONCLUSION

The Discrete Turn Algorithm may be particularly useful to characterize turns when the turn angle is unknown, i.e., during free-living conditions. The Merged Turn algorithm is recommended for clinical tasks in which the single-turn angle is known, especially for patients who hesitate while turning.

A multi-modal virtual reality treadmill intervention for enhancing mobility and cognitive function in people with multiple sclerosis: Protocol for a randomized controlled trial

BACKGROUND

Gait and cognitive impairments are common in individuals with Multiple Sclerosis (MS) and can interfere with everyday function. Those with MS have difficulties executing cognitive tasks and walking simultaneously, a reflection of dual-task interference. Therefore, dual-task training may improve functional ambulation. Additionally, using technology such as virtual reality can provide personalized rehabilitation while mimicking real-world environments. The purpose of this randomized controlled trial is to establish the benefits of a combined cognitive-motor virtual reality training on MS symptoms compared to conventional treadmill training.

METHODS

This study will be a single-blinded, two arm RCT with a six-week intervention period. 144 people with MS will be randomized into a treadmill training alone group or treadmill training with virtual reality group. Both groups will receive 18 sessions of training while walking on a treadmill, with the virtual reality group receiving feedback from the virtual system. Primary outcome measures include dual-task gait speed and information processing speed, which will be measured prior to training, one-week post-training, and three months following training.

DISCUSSION

This study will provide insight into the ability of a multi-modal cognitive-motor intervention to reduce dual-task cost and to enhance information processing speed in those with MS. This is one of the first studies that is powered to understand whether targeted dual-task training can improve MS symptoms and increase functional ambulation. We anticipate that those in the virtual reality group will have a significantly greater increase in dual-task gait speed and information processing speed than those achieved via treadmill training alone.

SPARC Metrics Provide Mobility Smoothness Assessment in Oldest-Old With and Without a History of Falls: A Case Control Study

Aging-related neuromuscular and neurocognitive decline induces unsmooth movements in daily functional mobility. Here, we used a robust analysis of linear and angular spectral arc length (SPARC) in the single and dual task instrumented timed up-and-go (iTUG) test to compare functional mobility smoothness in fallers and non-fallers aged 85 and older. 64 participants aged 85 and older took part in this case control study. The case group (fallers, n = 32) had experienced falls to the ground in the 6 months prior to the assessment. SPARC analyses were conducted in all phases of the single and dual task iTUGs. We also performed correlation mapping to test the relation of socio-demographic and clinical features on SPARC metrics. The magnitude of between-group differences was calculated using D-Cohen effect size (ES). SPARC was able to distinguish fallers during the single iTUG (ES ≈ 4.18). Turning while walking in the iTUG induced pronounced unsmooth movements in the fallers (SPARC ≈ −13; ES = 3.52) and was associated with the ability to maintain balance in the functional reach task. This information is of importance in the study of functional mobility in the oldest-old and to assess the efficacy of fall-prevention programs.

Visual dependence affects the motor behavior of older adults during the Timed Up and Go (TUG) test

BACKGROUND

Older adults show greater postural instabilities under misleading visual cues relative to younger adults. We investigated the effects of age-related visual dependence on motor performance under increased attention demands by adding a motor task and visual stimulus to the Timed Up and Go (TUG) test sub-components.

METHOD

We designed a cross-sectional quantitative study. Twenty-eight younger (n = 12) and older (n = 16) adults completed the TUG test while wearing a head-mounted display (HMD) that presented a visual stimulus and/or carrying a cup of water. Outcome measures were turning cadence; gait speed; pitch, yaw, and roll peak trunk velocities (PTVs); and acceleration ranges of sit-to-stand and stand-to-sit.

RESULTS

Wearing the HMD caused significant performance differences in the TUG test tasks due to age and visual dependence, although performance was lower across all groups with the HMD (p < 0.01). Older adults showed lower roll PTV in turning compared to younger adults (p = 0.03). Visually dependent older adults showed smaller mediolateral and vertical acceleration ranges (p < 0.04) in sit-to-stand compared to visually independent older adults.

CONCLUSION

The demand for orienting posture to a vertical position during sit-to-stand may differentiate older adults who are more visually dependent-and thus at greater fall risk- from those who are more visually independent. Age-related differences in turning behavior suggest a relationship with fall risk that warrants further investigation.

Pilot Study of the EncephaLog Smartphone Application for Gait Analysis

Gait disorders and falls are common in elders and in many clinical conditions, yet they are typically infrequently and subjectively evaluated, limiting prevention and intervention. Completion-time of the Timed-Up-and-Go (TUG) test is a well-accepted clinical biomarker for rating mobility and prediction of falls risk. Using smartphones’ integral accelerometers and gyroscopes, we already demonstrated that TUG completion-time can be accurately measured via a smartphone app. Here we present an extended app, EncephaLog^TM, which provides gait analysis in much more detail, offering 9 additional gait biomarkers on top of the TUG completion-time. In this pilot, four healthy adults participated in a total of 32 TUG tests; simultaneously recorded by EncephaLog and motion sensor devices used in movement labs: motion capture cameras (MCC), pressure mat; and/or wearable sensors. Results show high agreement between EncephaLog biomarkers and those measured by the other devices. These preliminary results suggest that EncephaLog can provide an accurate, yet simpler, instrumented TUG (iTUG) platform than existing alternatives, offering a solution for clinics that cannot afford the cost or space required for a dedicated motion lab and for monitoring patients at their homes. Further research on a larger study population with pathologies is required to assess full validity.

Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community

As people in developed countries live longer, assessing the fall risk becomes more important. A major contributor to the risk of elderly people falling is postural instability. This study aimed to use the multiscale entropy (MSE) analysis to evaluate postural stability during a timed-up-and-go (TUG) test. This test was deemed a promising method for evaluating fall risk among the elderly in a community. The MSE analysis of postural instability can identify the elderly prone to falling, whereupon early medical rehabilitation can prevent falls. Herein, an objective approach is developed for assessing the postural stability of 85 community-dwelling elderly people (aged 76.12 ± 6.99 years) using the short-form Berg balance scale. Signals were collected from the TUG test using a triaxial accelerometer. A segment-based TUG (sTUG) test was designed, which can be obtained according to domain knowledge, including “Sit-to-Walk (STW),” “Walk,” “Turning,” and “Walk-to-Sit (WTS)” segments. Employing the complexity index (CI) of sTUG can reveal information about the physiological dynamics’ signal for postural stability assessment. Logistic regression was used to assess the fall risk based on significant features of CI related to sTUG. MSE curves for subjects at risk of falling (n = 19) exhibited different trends from those not at risk of falling (n = 66). Additionally, the CI values were lower for subjects at risk of falling than those not at risk of falling. Results show that the area under the curve for predicting fall risk among the elderly subjects with complexity index features from the overall TUG test is 0.797, which improves to 0.853 with the sTUG test. For the elderly living in a community, early assessment of the CI for sTUG using MSE can help predict the fall risk.

Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20-70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

Different Combinations of Mobility Metrics Derived From a Wearable Sensor Are Associated With Distinct Health Outcomes in Older Adults

Background

Gait speed is a robust nonspecific predictor of health outcomes. We examined if combinations of gait speed and other mobility metrics are associated with specific health outcomes.

Methods

A sensor (triaxial accelerometer and gyroscope) placed on the lower back, measured mobility in the homes of 1,249 older adults (77% female; 80.0, SD = 7.72 years). Twelve gait scores were extracted from five performances, including (a) walking, (b) transition from sit to stand, (c) transition from stand to sit, (d) turning, and (e) standing posture. Using separate Cox proportional hazards models, we examined which metrics were associated with time to mortality, incident activities of daily living disability, mobility disability, mild cognitive impairment, and Alzheimer’s disease dementia. We used a single integrated analytic framework to determine which gait scores survived to predict each outcome.

Results

During 3.6 years of follow-up, 10 of the 12 gait scores predicted one or more of the five health outcomes. In further analyses, different combinations of 2–3 gait scores survived backward elimination and were associated with the five outcomes. Sway was one of the three scores that predicted activities of daily living disability but was not included in the final models for other outcomes. Gait speed was included along with other metrics in the final models predicting mortality and activities of daily living disability but not for other outcomes.

Conclusions

When analyzing multiple mobility metrics together, different combinations of mobility metrics are related to specific adverse health outcomes. Digital technology enhances our understanding of impaired mobility and may provide mobility biomarkers that predict distinct health outcomes.

Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults

INTRODUCTION

Mobility metrics derived from wearable sensor recordings are associated with parkinsonism in older adults. We examined if these metrics predict incident parkinsonism.

METHODS

Parkinsonism was assessed annually in 683 ambulatory, community-dwelling older adults without parkinsonism at baseline. Four parkinsonian signs were derived from a modified Unified Parkinson's Disease Rating Scale (UPDRS). Parkinsonism was based on the presence of 2 or more signs. Participants wore a sensor on their back while performing a 32 foot walk, standing posture, and Timed Up and Go (TUG) tasks. 12 mobility scores were extracted. Cox proportional hazards models with backward elimination were used to identify combinations of mobility scores independently associated with incident parkinsonism.

RESULTS

During follow-up of 2.5 years (SD = 1.28), 139 individuals developed parkinsonism (20.4%). In separate models, 6 of 12 mobility scores were individually associated with incident parkinsonism, including: Speed and Regularity (from 32 ft walk), Sway (from standing posture), and 3 scores from TUG subtasks (Posterior sit to stand transition, Range stand to sit transition, and Yaw, a measure of turning efficiency). When all mobility scores were analyzed together in a single model, 2 TUG subtask scores, Range from stand to sit transition (HR, 1.42, 95%CI, 1.09, 1.82) and Yaw from turning (HR, 0.56, 95%CI, 0.42, 0.73) were independently associated with incident parkinsonism. These results were unchanged when controlling for chronic health covariates.

CONCLUSION

Mobility metrics derived from a wearable sensor complement conventional gait testing and have potential to enhance risk stratification of older adults who may develop parkinsonism.

Extended Timed Up & Go test: Is walking forward and returning back to the chair equivalent gait?

The Timed Up & Go test (TUG) is functional test and is a part of routine clinical examinations. The instrumented Timed Up & Go test enables its segmentation to sub-tasks: sit-to-stand, walking forward, turning, walking back, stand-to-sit, and consequently the computation of task-specific parameters and sub-tasks separately. However, there are no data on whether walking forward parameters differ from the walking back parameters. This study tested the differences between walking forward and walking back in the TUG extended to 10 m for 17 spatio-temporal gait parameters. All parameters were obtained from a GAITRite® pressure sensitive walkway (CIR Systems, Inc.). The differences were assessed for healthy controls and Parkinson's disease (PD) patients. None of investigated parameters exhibited a difference between both gait subtasks for healthy subjects group. Five parameters of interest, namely velocity, step length, stride length, stride velocity, and the proportion of the double support phase with respect to gait cycle duration, showed a statistically significant difference between gait for walking forward and walking back in PD patients. Therefore, we recommend a separate assessment for walking forward and walking back rather than averaging both gaits together.

Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring

Background

The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait.

Methods

In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots.

Results

As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values (p < 0.001) and different (p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values (p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.

Conclusions

Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test

Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub–tasks with a single IMU automatically providing information about variables such as: duration of sub–tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting.

Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61⁻70 Years Old

Assessment of physical performance by standard clinical tests such as the 30-sec Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 ± 2.4 years, 87 females, median LLFDI 72.31, range: 44.33⁻100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60⁻0.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60⁻0.77) and 0.65 (95%CI 0.56⁻0.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61⁻70 years.

The transition between turning and sitting in patients with Parkinson's disease: A wearable device detects an unexpected sequence of events

BACKGROUND

When older adults turn to sit, about 80% of the subjects complete the turn before starting to sit i.e., a distinct-strategy, while in about 20%, part of the turning and sitting take place concurrently, i.e., an overlapping-strategy. A prolonged duration of the separation between tasks in the distinct-strategy (D-interval) and a prolonged duration of the overlap interval in overlapping-strategy (O-interval) are related to worse motor symptoms and poorer cognition. In the present study, we evaluated what strategy is employed by patients with Parkinson's disease (PD) when they transition from turning to sitting.

METHODS

96 participants with PD performed turn to sit as part of the Timed Up and Go test, both with and without medications, while wearing a body-fixed sensor. We quantified the turn-to-sit transition and determined which strategy (distinct or overlapping) was employed. We then stratified the cases and used regression models adjusted for age, gender, height, and weight to examine the associations of the D-interval or O-interval with parkinsonian features and cognition.

RESULTS

Most patients (66%) employed the overlapping-strategy, both off and on anti-parkinsonian medications. Longer O-intervals were associated with longer duration of PD, more severe PD motor symptoms, a higher postural-instability-gait-disturbance (PIGD) score, and worse freezing of gait. Longer D-intervals were not associated with disease duration or PD motor symptoms. Neither the D- nor O-intervals were related to cognitive function. Individuals who employed the overlapping-strategy had more severe postural instability (i.e., higher PIGD scores), as compared to those who used the distinct-strategy.

SIGNIFICANCE

In contrast to older adults without PD, most patients with PD utilize the overlapping strategy. Poorer postural and gait control are associated with the strategy choice and with the duration of concurrent performance of turning and sitting. Additional work is needed to further explicate the mechanisms underlying these strategies and their clinical implications.

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis

Introduction: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. Methods: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. Twenty-three healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI ≤ 2). Results: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction-representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. Discussion: We propose for PwMS that the diminished peak angular velocity during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. Conclusions: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine.

Validation of the ambient TUG chair with light barriers and force sensors in a clinical trial

To initiate appropriate interventions and avoid physical decline, comprehensive measurements are needed to detect functional changes in elderly people at the earliest possible stage. The established Timed Up&Go (TUG) test takes little time and, due to its standardized and easy procedure, can be conducted by elderly people in their own homes without clinical guidance. Therefore, cheap light barriers (LBs) and force sensors (FSs) are well suited ambient sensors that could easily be attached to existing (arm)chairs to measure and report TUG times in order to identify functional decline. We validated the sensitivity of these sensors in a clinical trial with 100 elderlies aged 58-92 years with a mean of 74 (±6.78) years by comparing the sensor-based results with standard TUG measurements using a stopwatch. We further evaluated the accuracy enhancement when calibrating the algorithm via a mixed linear model. With calibration, the LBs achieved a root mean square error (RMSE) of 0.83 s, compared to 1.90 s without, and the FSs achieved 0.90 s compared to 2.12 s without. The suitability of measuring accurate TUG times with each of the ambient sensors and of measuring TUG regularly in the homes of elderly people could be confirmed.

Criterion validity of the instrumented Timed Up and Go test: A partial least square regression study

The Timed Up and Go (TUG) test is a common mobility measure in rehabilitation. With the instrumental TUG test (ITUG; i.e. the TUG measured by inertial measurement units, IMUs), several movement measures are newly available. However, the clinical meaning of these new measures is not totally clear. Aim of the current work is to evaluate the validity of different ITUG parameters as a measure of balance. Neurological patients (n = 122; 52 females; 89 older than 65 years) completed the TUG test with IMUs secured to their back. IMUs signals were used to split the TUG test in five phases (sit-to-stand, walk1, turn1, walk2 and turn-and-sit) and twelve movement parameters were obtained. Experienced clinicians administered the Mini-BESTest (MB) scale, a sound balance measure. The partial least square regression (PLSR) was used to explore the association between the ITUG variables and the MB measure. A PLSR model with twelve ITUG variables had satisfactory fit parameters (RMSEP: 11%; R²: 0.41, 95% CI: 0.28-0.54; regression line: 1, 95% CI: 0.78-1.22). Three ITUG variables (i.e. turn1 vertical angular velocity, turn1 duration and turn2 vertical angular velocity) were found to be the most important predictors of the MB measure. A PLSR model with the turning variables only had fit parameters comparable to that of the twelve variables model. Turning parameters from the TUG test are good predictors of the MB scale. The mean angular velocity during turning and the duration of the turning phase are thus proposed as a valid, ratio-level measures of balance in neurological patients.

Religious Orders Study and Rush Memory and Aging Project

BACKGROUND

The Religious Orders Study and Rush Memory and Aging Project are both ongoing longitudinal clinical-pathologic cohort studies of aging and Alzheimer's disease (AD).

OBJECTIVES

To summarize progress over the past five years and its implications for understanding neurodegenerative diseases.

METHODS

Participants in both studies are older adults who enroll without dementia and agree to detailed longitudinal clinical evaluations and organ donation. The last review summarized findings through the end of 2011. Here we summarize progress and study findings over the past five years and discuss new directions for how these studies can inform on aging and AD in the future.

RESULTS

We summarize 1) findings on the relation of neurobiology to clinical AD; 2) neurobiologic pathways linking risk factors to clinical AD; 3) non-cognitive AD phenotypes including motor function and decision making; 4) the development of a novel drug discovery platform.

CONCLUSION

Complexity at multiple levels needs to be understood and overcome to develop effective treatments and preventions for cognitive decline and AD dementia.

Transition Between the Timed up and Go Turn to Sit Subtasks: Is Timing Everything?

OBJECTIVE

The Timed Up and Go (TUG), one of the most widely used tests of mobility, has been validated and associated with adverse outcomes in the community, acute care, and nursing home setting. It is composed of several distinct subtasks; however, the temporal relationship when transitioning between subtasks has not been well-studied. We tested the hypothesis that longer transition durations between the final turn to the sitting subtasks are associated with worse motor and cognitive performance in older adults.

METHODS

A total of 1055 participants (80.33 ± 7.57 years, 76.96% female) performed the TUG while wearing a 3-dimensional inertial sensor on their lower back. We employed a series of linear regressions to examine the association of the duration between the turn and sitting subtasks with clinical characteristics including motor and cognitive functions.

RESULTS

Participants employed 2 different strategies when they transitioned from turning to sitting. (1) Distinct transition strategy: 816 participants (77.34%) first completed the turn before starting to sit. The average duration between these distinct subtasks (D-interval) was 715 ± 980 ms. (2) Overlapping transition strategy: 239 participants (22.65%) started to sit before completing the turn. The average overlap duration between these tasks (O-interval) was 237 ± 269 ms. Participants who employed the distinct transition strategy were slightly younger than those who employed the overlapping transition strategy (P ≤ .013). Higher D-intervals and O-intervals were associated with worse TUG performance (P ≤ .02), with poorer motor and cognitive function, [ie, worse parkinsonian gait (P ≤ .001), lower level of perceptual speed (P ≤ .03), and with worse mobility disability (P ≤ .001)]. A longer D-interval was associated with worse gait speed and bradykinesia (P ≤ .001), whereas a longer O-interval was associated with increased rigidity (P = .004).

CONCLUSIONS

Older adults apparently employ 2 different strategies when transitioning from turning to sitting. The instrumented TUG can characterize additional gait and balance aspects that cannot be derived from traditional TUG assessments. These new measures offer novel targets for intervention to decrease the burden of late-life gait impairment.

Falls in Parkinson's disease: A complex and evolving picture

Falls are a major determinant of poor quality of life, immobilization, and reduced life expectancy in people affected by Parkinson's disease (PD) and in older adults more generally. Although many questions remain, recent research has advanced the understanding of this complex problem. The goal of this review is to condense new knowledge of falls in PD from prodromal to advanced disease, taking into account risk factors, assessment, and classification as well as treatment. The fundamental steps of clinical and research-based approaches to falls are described, namely, the identification of fall risk factors, clinical and instrumental methods to evaluate and classify fall risk, and the latest evidence to reduce or delay falls in PD. We summarize recent developments, the direction in which the field should be heading, and what can be recommended at this stage. We also provide a practical algorithm for clinicians.© 2017 International Parkinson and Movement Disorder Society.

Aging effect on the instrumented Timed-Up-and-Go test variables in nursing home women aged 80-93 years

Although the total “Timed-Up-and Go” test (TUG) performance time can characterize an age-related decline of general mobility, this result alone doesn’t give any detailed information about the test subtasks. The primary objective of the study was to identify in nursing home women a variable extracted from instrumented TUG (iTUG) that is the best predictor of age. The secondary objective was to assess whether this variable is associated with the results of the isometric knee extension peak torque (IKEPT); lower limb strength measured by the 30-s chair stand test (30sCST), and walking capacity measured by the 6-min walk test (6MWT). Twenty-six women (mean ± SD: age—85.8 ± 3.6 years; body weight—59.4 ± 12.3 kg; body height—151.0 ± 7.3 cm; BMI—26.0 ± 4.9 kg/m²) performed iTUG (while wearing a body-fixed inertial sensor) and functional tests. Total iTUG performance time significantly correlated with age (r = 0.484; p < 0.05), 30sCST (r = −0.593; p < 0.01), and 6MWT (r = −0.747; p < 0.001) but not with absolute nor relative IKEPT (p > 0.05). Additionally, the subjects’ age correlated with 30sCST (r = −0.422; p < 0.05), 6MWT (r = −0.482; p < 0.05), IKEPT (r = −0.392; p < 0.05) and IKEPT/FFM (r = −0.407; p < 0.05). Five out of 16 analyzed iTUG variables were significantly related to age, and multiple regression analysis showed the best correlation with the sit-to-stand vertical acceleration range (STSVAR) (r² = 0.430; SEE = 3.041; β = −0.544 ± 0.245; B = −1.204 ± 0.543; p < 0.05). Moreover, STSVAR was significantly associated with %Fat (r = 0.415; p < 0.05), 30sCST (r = 0.519; p < 0.01), 6MWT (r = 0.585; p < 0.01) but not with absolute nor relative IKEPT (p > 0.05). The obtained results suggest that in the oldest old group of nursing home women an age-related decline in TUG performance is mainly associated with a reduction of “explosive” strength of lower limb muscles.

Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go

Devices and sensors for identification of fallers can be used to implement actions to prevent falls and to allow the elderly to live an independent life while reducing the long-term care costs. In this study we aimed to investigate the accuracy of Timed Up and Go test, for fallers’ identification, using fusion of features extracted from accelerometer data. Single and dual tasks TUG (manual and cognitive) were performed by a final sample (94% power) of 36 community dwelling healthy older persons (18 fallers paired with 18 non-fallers) while they wear a single triaxial accelerometer at waist with sampling rate of 200Hz. The segmentation of the TUG different trials and its comparative analysis allows to better discriminate fallers from non-fallers, while conventional functional tests fail to do so. In addition, we show that the fusion of features improve the discrimination power, achieving AUC of 0.84 (Sensitivity = Specificity = 0.83, 95% CI 0.62-0.91), and demonstrating the clinical relevance of the study. We concluded that features extracted from segmented TUG trials acquired with dual tasks has potential to improve performance when identifying fallers via accelerometer sensors, which can improve TUG accuracy for clinical and epidemiological applications.

Timed Up-and-Go phase segmentation in Parkinson's disease patients using unobtrusive inertial sensors

A widely accepted functional motor test for measuring basic mobility capabilities is the `Timed Up-and-Go' (TUG) test. Although several basic mobility tasks are included, only the total time is used as outcome parameter. It has been shown that timings of sub-phases can be used as relevant clinical parameters for the assessment of Parkinson's disease patients. A variety of systems and methods have been proposed for instrumenting the TUG test, but only limited information has been published regarding phase classification. In this paper an automated TUG phase classification methodology is proposed and validated in a study with 16 Parkinson's disease patients. Statistical, signal energy, chronological and gait features were extracted from acceleration and orientation signals of shoe mounted inertial measurement units. The phases `sit to walk', `walking', `first turn', `second turn' and `turn to sit' were segmented in a two stage classifier approach. Strides were used for a separation of the walking phase and classifiers like NaiveBayes, k-Nearest-Neighbor, Support Vector Machine (SVM) and Random Forest for the final phase segmentation. SVM performed best with a mean sensitivity of 81.80% over all phases. Additionally, the impact of UPDRS and Hoehn & Yahr ratings on the phase times was assessed. The proposed methodology could be used to analyze gait parameters of sub-phases like stride length, stride time, foot clearance, heel-strike or toe-off angle for an improved assessment of Parkinson's disease patients.

Actigraphy features for predicting mobility disability in older adults

Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mobility impairment and major mobility disability (MMD). Men (N  =  357) and women (N  =  778) aged 70-89 years wore a tri-axial accelerometer (Actigraph GT3X) on the right hip during free-living conditions for 8.4  ±  3.0 d. One-second epoch data were summarized into 67 features. Several machine learning techniques were used to select features from the free-living condition to predict mobility impairment, defined as 400 m walking speed  <0.80 m s^-1. Selected features were also included in a model to predict the first occurrence of MMD-defined as the loss in the ability to walk 400 m. Each method yielded a similar estimate of 400 m walking speed with a root mean square error of ~0.07 m s^-1 and an R-squared values ranging from 0.37-0.41. Sensitivity and specificity of identifying slow walkers was approximately 70% and 80% for all methods, respectively. The top five features, which were related to movement pace and amount (activity counts and steps), length in activity engagement (bout length), accumulation patterns of activity, and movement variability significantly improved the prediction of MMD beyond that found with common covariates (age, diseases, anthropometry, etc). This study identified a subset of actigraphy features collected in free-living conditions that are moderately accurate in identifying persons with clinically-assessed mobility impaired and significantly improve the prediction of MMD. These findings suggest that the combination of features as opposed to a specific feature is important to consider when choosing features and/or combinations of features for prediction of mobility phenotypes in older adults.

Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.

Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor

Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson's disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.

Intra-Rater, Inter-Rater and Test-Retest Reliability of an Instrumented Timed Up and Go (iTUG) Test in Patients with Parkinson's Disease

BACKGROUND

The "Timed Up and Go" (TUG) is a widely used measure of physical functioning in older people and in neurological populations, including Parkinson's Disease. When using an inertial sensor measurement system (instrumented TUG [iTUG]), the individual components of the iTUG and the trunk kinematics can be measured separately, which may provide relevant additional information.

OBJECTIVE

The aim of this study was to determine intra-rater, inter-rater and test-retest reliability of the iTUG in patients with Parkinson's Disease.

METHODS

Twenty eight PD patients, aged 50 years or older, were included. For the iTUG the DynaPort Hybrid (McRoberts, The Hague, The Netherlands) was worn at the lower back. The device measured acceleration and angular velocity in three directions at a rate of 100 samples/s. Patients performed the iTUG five times on two consecutive days. Repeated measurements by the same rater on the same day were used to calculate intra-rater reliability. Repeated measurements by different raters on the same day were used to calculate intra-rater and inter-rater reliability. Repeated measurements by the same rater on different days were used to calculate test-retest reliability.

RESULTS

Nineteen ICC values (15%) were ≥ 0.9 which is considered as excellent reliability. Sixty four ICC values (49%) were ≥ 0.70 and < 0.90 which is considered as good reliability. Thirty one ICC values (24%) were ≥ 0.50 and < 0.70, indicating moderate reliability. Sixteen ICC values (12%) were ≥ 0.30 and < 0.50 indicating poor reliability. Two ICT values (2%) were < 0.30 indicating very poor reliability.

CONCLUSIONS

In conclusion, in patients with Parkinson's disease the intra-rater, inter-rater, and test-retest reliability of the individual components of the instrumented TUG (iTUG) was excellent to good for total duration and for turning durations, and good to low for the sub durations and for the kinematics of the SiSt and StSi. The results of this fully automated analysis of instrumented TUG movements demonstrate that several reliable TUG parameters can be identified that provide a basis for a more precise, quantitative use of the TUG test, in clinical practice.

Motor Function Is Associated With Incident Disability in Older African Americans

BACKGROUND

Disability in older African American adults is common, but its basis is unclear. We tested the hypothesis that the level of motor function is associated with incident disability in older African Americans after adjusting for cognition.

METHODS

A prospective observational cohort study of 605 older community-dwelling African American adults without dementia was carried out. Baseline global motor score summarized 11 motor performances, cognition was based on 19 cognitive tests, and self-reported disability was obtained annually. We examined the association of motor function with incident disability (instrumental activities of daily living [IADL], activities of daily living [ADL], and mobility disability) with a series of Cox proportional hazards models which controlled for age, sex, and education.

RESULTS

Average follow-up was about 5 years. In proportional hazards models, a 1-SD increase in baseline level of global motor score was associated with about a 50% decrease in the risk of subsequent IADL, ADL, and mobility disability (all p values < .001). These associations were unchanged in analyses controlling for cognition and other covariates. Further, the association of global motor score and incident ADL disability varied with the level of cognition (estimate -5.541, SE 1.634, p < .001), such that higher motor function was more protective at higher levels of cognition. Mobility and dexterity components of global motor score were more strongly associated with incident disability than strength (all p values < .001).

CONCLUSIONS

Better motor function in older African Americans is associated with a decreased risk of developing disability. Moreover, the association of motor function and disability is stronger in individuals with better cognitive function.

A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson's Disease

Background

A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs.

Methods

The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously.

Results

Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes.

Conclusion

These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Technical and clinical view on ambulatory assessment in Parkinson's disease

With the progress of technologies of recent years, methods have become available that use wearable sensors and ambulatory systems to measure aspects of--particular axial--motor function. As Parkinson's disease (PD) can be considered a model disorder for motor impairment, a significant number of studies have already been performed with these patients using such techniques. In general, motion sensors such as accelerometers and gyroscopes are used, in combination with lightweight electronics that do not interfere with normal human motion. A fundamental advantage in comparison with usual clinical assessment is that these sensors allow a more quantitative, objective, and reliable evaluation of symptoms; they have also significant advantages compared to in-lab technologies (e.g., optoelectronic motion capture) as they allow long-term monitoring under real-life conditions. In addition, based on recent findings particularly from studies using functional imaging, we learned that non-motor symptoms, specifically cognitive aspects, may be at least indirectly assessable. It is hypothesized that ambulatory quantitative assessment strategies will allow users, clinicians, and scientists in the future to gain more quantitative, unobtrusive, and everyday relevant data out of their clinical evaluation and can also be designed as pervasive (everywhere) and intensive (anytime) tools for ambulatory assessment and even rehabilitation of motor and (partly) non-motor symptoms in PD.

Clinical evaluation of a mobile sensor-based gait analysis method for outcome measurement after knee arthroplasty

Clinical scores and motion-capturing gait analysis are today's gold standard for outcome measurement after knee arthroplasty, although they are criticized for bias and their ability to reflect patients' actual quality of life has been questioned. In this context, mobile gait analysis systems have been introduced to overcome some of these limitations. This study used a previously developed mobile gait analysis system comprising three inertial sensor units to evaluate daily activities and sports. The sensors were taped to the lumbosacral junction and the thigh and shank of the affected limb. The annotated raw data was evaluated using our validated proprietary software. Six patients undergoing knee arthroplasty were examined the day before and 12 months after surgery. All patients reported a satisfactory outcome, although four patients still had limitations in their desired activities. In this context, feasible running speed demonstrated a good correlation with reported impairments in sports-related activities. Notably, knee flexion angle while descending stairs and the ability to stop abruptly when running exhibited good correlation with the clinical stability and proprioception of the knee. Moreover, fatigue effects were displayed in some patients. The introduced system appears to be suitable for outcome measurement after knee arthroplasty and has the potential to overcome some of the limitations of stationary gait labs while gathering additional meaningful parameters regarding the force limits of the knee.

Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review

Clinicians commonly use questionnaires and tests based on daily life activities to evaluate physical function. However, the outcomes are usually more qualitative than quantitative and subtle differences are not detectable. In this review, we aim to assess the role of body motion sensors in physical performance evaluation, especially for the sit-to-stand and stand-to-sit transitions. In total, 53 full papers and conference abstracts on related topics were included and 16 different parameters related to transition performance were identified as potentially meaningful to explain certain disabilities and impairments. Transition duration is the most used to evaluate chair-related tests in real clinical settings. High-fall-risk fallers and frail subjects presented longer and more variable transition duration. Other kinematic parameters have also been highlighted in the literature as potential means to detect age-related impairments. In particular, vertical linear velocity and trunk tilt range were able to differentiate between different frailty levels. Frequency domain measures such as spectral edge frequency were also higher for elderly fallers. Lastly, approximate entropy values were larger for subjects with Parkinson's disease and were significantly reduced after treatment. This information could help clinicians in their evaluations as well as in prescribing a physical fitness program to correct a specific deficit.